Why do we need to flip the kernel in 2D convolution?

Basically it's because time goes along the x axis with the small time values on the left and the big (later) time values on the right. So if you start shifting in, you're having the big time values hit your signal first, which is not right (causal). So you have to flip it to make the small time values shift in first.

1. Why do you flip the kernel in convolution?
2. Do you need to flip kernel in convolution?
3. What does this kernel do in a convolution?
4. What is a 2D kernel?

Why do you flip the kernel in convolution?

When performing the convolution, you want the kernel to be flipped with respect to the axis along which you're performing the convolution because if you don't, you end up computing a correlation of a signal with itself.

Do you need to flip kernel in convolution?

In Convolution operation, the kernel is first flipped by an angle of 180 degrees and is then applied to the image.

What does this kernel do in a convolution?

Convolution is using a 'kernel' to extract certain 'features' from an input image. Let me explain. A kernel is a matrix, which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner.

What is a 2D kernel?

The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.